Open Set RF Fingerprinting Identification: A Joint Prediction and Siamese Comparison Framework

• URL: http://arxiv.org/abs/2501.15391v1
• Date: Sun, 26 Jan 2025 04:09:07 GMT
• Title: Open Set RF Fingerprinting Identification: A Joint Prediction and Siamese Comparison Framework
• Authors: Donghong Cai, Jiahao Shan, Ning Gao, Bingtao He, Yingyang Chen, Shi Jin, Pingzhi Fan,
• Abstract summary: We propose a joint radio frequency fingerprint prediction and siamese comparison (JRFFP-SC) framework for open set recognition. The proposed JRFFP-SC framework eliminates inter-class interference and effectively addresses the challenges associated with open set identification.
• Score: 37.79439245394741
• License:
• Abstract: Radio Frequency Fingerprinting Identification (RFFI) is a lightweight physical layer identity authentication technique. It identifies the radio-frequency device by analyzing the signal feature differences caused by the inevitable minor hardware impairments. However, existing RFFI methods based on closed-set recognition struggle to detect unknown unauthorized devices in open environments. Moreover, the feature interference among legitimate devices can further compromise identification accuracy. In this paper, we propose a joint radio frequency fingerprint prediction and siamese comparison (JRFFP-SC) framework for open set recognition. Specifically, we first employ a radio frequency fingerprint prediction network to predict the most probable category result. Then a detailed comparison among the test sample's features with registered samples is performed in a siamese network. The proposed JRFFP-SC framework eliminates inter-class interference and effectively addresses the challenges associated with open set identification. The simulation results show that our proposed JRFFP-SC framework can achieve excellent rogue device detection and generalization capability for classifying devices.

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